Plots
Joao Marreiros
2021-04-22 14:07:04
Brief description of the script
This R markdown document plots the data of the resulting CSV file from the computing ISO 25178-2 parameters in ConfoMap. These data is part of the manuscript: Dubreuil et al. A ‘family of wear’: Exploring use-wear patterns on ad hoc burnishing tools
This R project and respective scripts follow the procedures described by Marwick et al. 2017.
The authors would like to thank Ivan Calandra and Lisa Schunk for their help and contribution on several chunks of code included here in the script (pieces of code are also adapated from Calandra et al. 2019, Pedergnana et al. 2020a, 2020b).
To compile this markdown document do not delete or move files from their original folders.
For any questions, comments and inputs, please contact:
Joao Marreiros, marreiros@rgzm.de
dir_in <- "analysis/derived_data/"
dir_out <- "analysis/plots"Raw data must be located in ~/analysis/derived_data/.
Formatted data will be saved in ~/analysis/plots. The knit directory for this script is the project directory.
1 Load packages
library(R.utils)
library(ggplot2)
library(tools)
library(tidyverse)
library(wesanderson)2 Get name, path and information of the file
data_file <- list.files(dir_in, pattern = "\\.Rbin$", full.names = TRUE)
md5_in <- md5sum(data_file)
info_in <- data.frame(file = basename(names(md5_in)), checksum = md5_in, row.names = NULL)The checksum (MD5 hashes) of the imported file is:
file checksum
1 MSTRdata.Rbin 422ee34326fad0785833a2fb4d402bab3 Load data into R object
imp_data <- loadObject(data_file)
str(imp_data)'data.frame': 30 obs. of 53 variables:
$ Sample.ID : chr "Kremasti4" "Kremasti4" "Kremasti4" "Kremasti4" ...
$ Microscope : chr "LSM" "LSM" "LSM" "LSM" ...
$ Objective : chr "50x" "50x" "50x" "50x" ...
$ PolishType : chr "natural" "natural" "natural" "natural" ...
$ Surface : chr "a" "b" "c" "d" ...
$ Topo : chr "Topo" "Topo" "Topo" "Topo" ...
$ Acquisition.Date : chr "2021/04/15" "2021/04/15" "2021/04/15" "2021/04/15" ...
$ Analysis.Date : chr "14:06:02" "14:08:39" "14:11:12" "14:13:40" ...
$ Analysis.Time : chr "4/15/2021 10:24:41 AM" "4/15/2021 10:51:24 AM" "4/15/2021 11:45:47 AM" "4/15/2021 12:01:50 PM" ...
$ Axis.length.X : num 255 255 255 255 255 ...
$ Axis.size.X : num 3000 3000 3000 3000 3000 3000 3000 3000 3000 3000 ...
$ Axis.spacing.X : num 85.2 85.2 85.2 85.2 85.2 ...
$ Axis.length.Y : num 255 255 255 255 255 ...
$ Axis.size.Y : num 3000 3000 3000 3000 3000 3000 3000 3000 3000 3000 ...
$ Axis.spacing.Y : num 85.2 85.2 85.2 85.2 85.2 ...
$ Axis.length.Z : num 40.7 49.9 92.6 31.8 29.3 ...
$ Axis.size.Z : num 65532 65532 65531 65532 65531 ...
$ Axis.spacing.Z : num 0.621 0.761 1.413 0.485 0.447 ...
$ NM.points.ratio.Z : num 0 0 0 0 0 0 0 0 0 0 ...
$ Sq : num 1.58 4.09 1.47 2.24 1.77 ...
$ Ssk : num -0.61 -0.391 -0.274 -0.049 -0.929 ...
$ Sku : num 4.88 2.53 6.23 3.44 5.87 ...
$ Sp : num 5.47 10.44 5.15 9.02 7.91 ...
$ Sv : num 6.86 12.48 8.2 7.06 8.52 ...
$ Sz : num 12.3 22.9 13.4 16.1 16.4 ...
$ Sa : num 1.13 3.4 1.08 1.77 1.25 ...
$ Smr : num 0.484 0.239 0.604 0.207 0.126 ...
$ Smc : num 1.69 4.56 1.72 2.87 1.66 ...
$ Sxp : num 3.99 9.02 2.67 4.65 4.7 ...
$ Sal : num 19.5 32.4 23.5 30.6 20.7 ...
$ Str : num 0.48 NA NA 0.614 0.813 ...
$ Std : num 42.2 93.2 33 25.3 62 ...
$ Sdq : num 0.383 0.658 0.521 0.403 0.403 ...
$ Sdr : num 6 15.94 8.53 6.73 6.56 ...
$ Vm : num 0.0895 0.1529 0.1057 0.0994 0.0945 ...
$ Vv : num 1.78 4.72 1.82 2.97 1.75 ...
$ Vmp : num 0.0895 0.1529 0.1057 0.0994 0.0945 ...
$ Vmc : num 1.13 4.14 1.09 1.84 1.28 ...
$ Vvc : num 1.5 4.24 1.65 2.68 1.41 ...
$ Vvv : num 0.287 0.478 0.17 0.292 0.343 ...
$ Maximum.depth.of.furrows: num 7.22 10.88 9.43 6.76 8.68 ...
$ Mean.depth.of.furrows : num 1.57 3.13 1.47 1.9 1.54 ...
$ Mean.density.of.furrows : num 3750 3056 4011 3480 3423 ...
$ First.direction : num 89.9772 90.014 45.0229 0.0123 44.9941 ...
$ Second.direction : num 45 135 180 26.5 63.5 ...
$ Third.direction : num 180 45 33.7 90 90 ...
$ Texture.isotropy : num 74 82.7 77.8 90.3 92.3 ...
$ epLsar : num NA NA NA NA NA NA NA NA NA NA ...
$ NewEplsar : num NA NA NA NA NA NA NA NA NA NA ...
$ Asfc : num 9.93 25.92 17.49 11.21 10.47 ...
$ Smfc : num 6281985 10723090 4628049 7318909 11574299 ...
$ HAsfc9 : num 0.539 0.39 1.927 0.603 0.546 ...
$ HAsfc81 : num 0.87 0.638 2.369 0.728 0.848 ...The imported file is: “~/analysis/derived_data//MSTRdata.Rbin”
4 Prepare variables
4.1 Define numeric variables
num.var <- 20:length(imp_data)The following variables will be used:
[20] Sq
[21] Ssk
[22] Sku
[23] Sp
[24] Sv
[25] Sz
[26] Sa
[27] Smr
[28] Smc
[29] Sxp
[30] Sal
[31] Str
[32] Std
[33] Sdq
[34] Sdr
[35] Vm
[36] Vv
[37] Vmp
[38] Vmc
[39] Vvc
[40] Vvv
[41] Maximum.depth.of.furrows
[42] Mean.depth.of.furrows
[43] Mean.density.of.furrows
[44] First.direction
[45] Second.direction
[46] Third.direction
[47] Texture.isotropy
[48] epLsar
[49] NewEplsar
[50] Asfc
[51] Smfc
[52] HAsfc9
[53] HAsfc815 Plot each of the selected numeric variables
5.1 Plot just original samples/surfaces organized by parameter
for (i in num.var) {
p <- ggplot(data = imp_data, aes_string(x = "Sample.ID", y = names(imp_data)[i],
colour = "PolishType")) +
geom_point(size = 1) +
geom_jitter(width = 0.25) +
theme_classic() +
labs(colour = "PolishType") +
labs(x = "Sample", y = gsub("\\.", " ", names(imp_data)[i])) +
scale_colour_hue(h = c(25,225), limits = levels(imp_data[["Surface"]]))
print(p)
# saves the plots
file_out <- paste0(file_path_sans_ext(info_in[["file"]]), "_plot_",
names(imp_data)[i], ".pdf")
ggsave(filename = file_out, plot = p, path = dir_out, device = "pdf", width = 26,
height = 21, units = "cm" )
}
Warning: Removed 6 rows containing missing values (geom_point).
Warning: Removed 6 rows containing missing values (geom_point).
Warning: Removed 6 rows containing missing values (geom_point).
Warning: Removed 6 rows containing missing values (geom_point).
Warning in min(x): no non-missing arguments to min; returning InfWarning in max(x): no non-missing arguments to max; returning -InfWarning in min(diff(sort(x))): no non-missing arguments to min; returning InfWarning in min(x): no non-missing arguments to min; returning InfWarning in max(x): no non-missing arguments to max; returning -InfWarning in stats::runif(length(x), -amount, amount): NAs producedWarning: Removed 30 rows containing missing values (geom_point).
Warning: Removed 30 rows containing missing values (geom_point).Warning in min(x): no non-missing arguments to min; returning InfWarning in max(x): no non-missing arguments to max; returning -InfWarning in min(diff(sort(x))): no non-missing arguments to min; returning InfWarning in min(x): no non-missing arguments to min; returning InfWarning in max(x): no non-missing arguments to max; returning -InfWarning in stats::runif(length(x), -amount, amount): NAs producedWarning: Removed 30 rows containing missing values (geom_point).
Warning: Removed 30 rows containing missing values (geom_point).
Warning in min(x): no non-missing arguments to min; returning InfWarning in max(x): no non-missing arguments to max; returning -InfWarning in min(diff(sort(x))): no non-missing arguments to min; returning InfWarning in min(x): no non-missing arguments to min; returning InfWarning in max(x): no non-missing arguments to max; returning -InfWarning in stats::runif(length(x), -amount, amount): NAs producedWarning: Removed 30 rows containing missing values (geom_point).
Warning: Removed 30 rows containing missing values (geom_point).Warning in min(x): no non-missing arguments to min; returning InfWarning in max(x): no non-missing arguments to max; returning -InfWarning in min(diff(sort(x))): no non-missing arguments to min; returning InfWarning in min(x): no non-missing arguments to min; returning InfWarning in max(x): no non-missing arguments to max; returning -InfWarning in stats::runif(length(x), -amount, amount): NAs producedWarning: Removed 30 rows containing missing values (geom_point).
Warning: Removed 30 rows containing missing values (geom_point).
5.2 Scatterplots of selected variables combined with Sample.ID and Polish type
5.2.1 Sa vs. Sq
#plot
Sa_Sq <- ggplot(data = imp_data) +
geom_point(mapping = aes(x = Sa, y = Sq, colour = PolishType)) +
theme_classic() +
labs(colour = "Surface") +
facet_wrap(~ Sample.ID) +
scale_colour_hue(h = c(25, 230))
print(Sa_Sq)
# saving the plot
file_out <- paste0(file_path_sans_ext(info_in[["file"]]), "_scatterplot_Sa-Sq", ".pdf")
ggsave(filename = file_out, plot = Sa_Sq, path = dir_out, device = "pdf")5.2.2 Sq vs. Vmc
Sq_Vmc <- ggplot(data = imp_data) +
geom_point(mapping = aes(x = Sq, y = Vmc, colour = PolishType)) +
theme_classic() +
labs(colour = "Surface") +
facet_wrap(~ Sample.ID) +
scale_colour_hue(h = c(25, 230))
print(Sq_Vmc)
file_out <- paste0(file_path_sans_ext(info_in[["file"]]), "_scatterplot_Sq-Vmc", ".pdf")
ggsave(filename = file_out, plot = Sq_Vmc, path = dir_out, device = "pdf")5.2.3 Mean depth of furrows vs. mean density of furrows
furrows <- ggplot(data = imp_data) +
geom_point(mapping = aes(x = Mean.depth.of.furrows, y = Mean.density.of.furrows, colour = PolishType)) +
theme_classic() +
labs(colour = "Surface", x = "Mean depth of furrows", y = "Mean density of furrows") +
facet_wrap(~ Sample.ID) +
scale_colour_hue(h = c(25, 230))
print(furrows)
file_out <- paste0(file_path_sans_ext(info_in[["file"]]), "_scatterplot_furrows", ".pdf")
ggsave(filename = file_out, plot = furrows, path = dir_out, device = "pdf")6 Show plot files information
info_out <- list.files(path = dir_out, pattern = "\\.pdf$", full.names = TRUE) %>%
md5sum()The checksum (MD5 hashes) of the exported files are:
analysis/plots/MSTRdata_plot_Asfc.pdf
"f1ebbf3109cc8f08a09e6c1f619dc3c1"
analysis/plots/MSTRdata_plot_epLsar.pdf
"542785e2614c12ec913e1cefadebddb9"
analysis/plots/MSTRdata_plot_First.direction.pdf
"94b125b82789044ad1ad8e2ac7d026ad"
analysis/plots/MSTRdata_plot_HAsfc81.pdf
"638d9c86bace804da565a17b3e2e2a37"
analysis/plots/MSTRdata_plot_HAsfc9.pdf
"02049129a42d09196d000550e916b377"
analysis/plots/MSTRdata_plot_Maximum.depth.of.furrows.pdf
"c4a9455f5661246a5d0aca8a975b7ed1"
analysis/plots/MSTRdata_plot_Mean.density.of.furrows.pdf
"9bd5db488308ac3287ee05826ee5f6f8"
analysis/plots/MSTRdata_plot_Mean.depth.of.furrows.pdf
"8403d6cbfc9d29c74484b10c9ba6c3d7"
analysis/plots/MSTRdata_plot_NewEplsar.pdf
"dbba892039125ddaba80abc5c5ef19c9"
analysis/plots/MSTRdata_plot_Sa.pdf
"6c17db31d4901d3f03ea16dee4dcf9aa"
analysis/plots/MSTRdata_plot_Sal.pdf
"8c1ac733e8a167497008008e4352bf0c"
analysis/plots/MSTRdata_plot_Sdq.pdf
"b4fff8cee7b621228368fe8acf344be4"
analysis/plots/MSTRdata_plot_Sdr.pdf
"ec8485941120bcf6dd1ad14be9e534a0"
analysis/plots/MSTRdata_plot_Second.direction.pdf
"7ababe46aae60a9265c8f81af6574290"
analysis/plots/MSTRdata_plot_Sku.pdf
"55ba60037f3abfd61fdc876e09149893"
analysis/plots/MSTRdata_plot_Smc.pdf
"3b457c59f3f59514595a77dc9c3d38f6"
analysis/plots/MSTRdata_plot_Smfc.pdf
"0b2e946e82aa1cb057644e4fae529a20"
analysis/plots/MSTRdata_plot_Smr.pdf
"53bb106b70484f204f6a0580490470d7"
analysis/plots/MSTRdata_plot_Sp.pdf
"38e8b7aa0358c4bd9047793b40a9f7f2"
analysis/plots/MSTRdata_plot_Sq.pdf
"33e6171909238156c2f2aec8aef73867"
analysis/plots/MSTRdata_plot_Ssk.pdf
"2c71eda1baebe5518b636beb237ae467"
analysis/plots/MSTRdata_plot_Std.pdf
"f3d3e21658947d9e9c4f9ecf367c6421"
analysis/plots/MSTRdata_plot_Str.pdf
"05309504308475aa36eac902110fc3f3"
analysis/plots/MSTRdata_plot_Sv.pdf
"ecad7c924aaeb371986195e3f2fca0ac"
analysis/plots/MSTRdata_plot_Sxp.pdf
"5767613cb303052a508347732c4fb4fc"
analysis/plots/MSTRdata_plot_Sz.pdf
"c46825bfc0b6495e332dcc73d568309b"
analysis/plots/MSTRdata_plot_Texture.isotropy.pdf
"9c118cfcba42901660a0aa6678fc0d0f"
analysis/plots/MSTRdata_plot_Third.direction.pdf
"52cac2edfcf9e3265af4148aba55cf9d"
analysis/plots/MSTRdata_plot_Vm.pdf
"82d82dafc672bd604c0da1a6af48fb9b"
analysis/plots/MSTRdata_plot_Vmc.pdf
"2c16b70d5baf9c8770a49aca16909362"
analysis/plots/MSTRdata_plot_Vmp.pdf
"58d0c2a4e704182488f3d1a4cd9be99e"
analysis/plots/MSTRdata_plot_Vv.pdf
"6c3d6c9d8826278d200b75d249a6719a"
analysis/plots/MSTRdata_plot_Vvc.pdf
"55e6c5f0e4aaebb0b37723a6a92894b7"
analysis/plots/MSTRdata_plot_Vvv.pdf
"3886feb12afb1b7699ebb74aeffaccc8"
analysis/plots/MSTRdata_scatterplot_furrows.pdf
"bd099d0ba9a09165ad67fc819da42bcc"
analysis/plots/MSTRdata_scatterplot_Sa-Sq.pdf
"85c050962c96c9cbe25d8b32bd4807df"
analysis/plots/MSTRdata_scatterplot_Sq-Vmc.pdf
"0e9ae22d56e2ab707cf2bf583b6945a0" 7 sessionInfo() and RStudio version
sessionInfo()R version 4.0.4 (2021-02-15)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Catalina 10.15.7
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] tools stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] wesanderson_0.3.6 forcats_0.5.1 stringr_1.4.0 dplyr_1.0.5
[5] purrr_0.3.4 readr_1.4.0 tidyr_1.1.3 tibble_3.1.1
[9] tidyverse_1.3.1 ggplot2_3.3.3 R.utils_2.10.1 R.oo_1.24.0
[13] R.methodsS3_1.8.1
loaded via a namespace (and not attached):
[1] Rcpp_1.0.6 lubridate_1.7.10 assertthat_0.2.1 rprojroot_2.0.2
[5] digest_0.6.27 utf8_1.2.1 R6_2.5.0 cellranger_1.1.0
[9] backports_1.2.1 reprex_2.0.0 evaluate_0.14 httr_1.4.2
[13] highr_0.9 pillar_1.6.0 rlang_0.4.10 readxl_1.3.1
[17] rstudioapi_0.13 jquerylib_0.1.3 rmarkdown_2.7 labeling_0.4.2
[21] munsell_0.5.0 broom_0.7.6 compiler_4.0.4 modelr_0.1.8
[25] xfun_0.22 pkgconfig_2.0.3 htmltools_0.5.1.1 tidyselect_1.1.0
[29] fansi_0.4.2 crayon_1.4.1 dbplyr_2.1.1 withr_2.4.2
[33] grid_4.0.4 jsonlite_1.7.2 gtable_0.3.0 lifecycle_1.0.0
[37] DBI_1.1.1 magrittr_2.0.1 scales_1.1.1 cli_2.4.0
[41] stringi_1.5.3 farver_2.1.0 fs_1.5.0 xml2_1.3.2
[45] bslib_0.2.4 ellipsis_0.3.1 generics_0.1.0 vctrs_0.3.7
[49] glue_1.4.2 hms_1.0.0 yaml_2.2.1 colorspace_2.0-0
[53] rvest_1.0.0 knitr_1.32 haven_2.4.0 sass_0.3.1 END OF SCRIPT